Sentinel: Advancing Automated Data Quality Checks in OHDSI Networks
Presented at OHDSI Europe 2025, Sentinel is a framework that continuously monitors and enhances data quality across OMOP CDM and NLP outputs. A live demo was shared with a select audience, showcasing how Sentinel supports faster, more transparent, and reproducible research across hospital networks.
Authors: Jana Van Canneyt, Nicolas De Roover, Karen D'Haeseleer, Dries Hens, Clara L. Oeste
High-quality EHR data is critical for reliable research, but traditional manual extraction can require more than 2000 hours per study. Sentinel is a framework designed to continuously monitor and enhance EHR data quality. It combines OMOP CDM structure and NLP outputs to monitor completeness, plausibility, and conformance.
By reducing manual review and improving reproducibility, Sentinel helps accelerate insight generation and supports the goals of EHDS and OHDSI-aligned research.
Complete the form to download the poster and discover the methods used and the results obtained.
High-quality EHR data is critical for reliable research, but traditional manual extraction can require more than 2000 hours per study. Sentinel is a framework designed to continuously monitor and enhance EHR data quality. It combines OMOP CDM structure and NLP outputs to monitor completeness, plausibility, and conformance.
By reducing manual review and improving reproducibility, Sentinel helps accelerate insight generation and supports the goals of EHDS and OHDSI-aligned research.
Complete the form to download the poster and discover the methods used and the results obtained.
